Total 31,058 skills, AI & Machine Learning has 5022 skills
Showing 12 of 5022 skills
Evaluate how well a codebase supports autonomous AI development. Analyzes repositories across eight technical pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery) and five maturity levels. Use when users request `/readiness-report` or want to assess agent readiness, codebase maturity, or identify gaps preventing effective AI-assisted development.
Triages GitHub issues by routing to oncall teams, applying labels, and closing questions. Use when processing new PyTorch issues or when asked to triage an issue.
Create Claude skills from book content (markdown files). Transforms long-form book knowledge into structured, context-efficient skill packages with granular reference files, workflows, and use-case guidelines. Use this skill when: - Converting a book (markdown) into a reusable Claude skill - Creating knowledge bases from technical books, guides, or documentation - Building skills that need progressive disclosure of large content - Structuring book knowledge for efficient context loading
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
Amazon Bedrock AgentCore multi-agent orchestration with Agent-to-Agent (A2A) protocol. Supervisor-worker patterns, agent collaboration, and hierarchical delegation. Use when building multi-agent systems, orchestrating specialized agents, or implementing complex workflows.
Guide developers through creating ChatGPT and MCP apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/widgets, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a ChatGPT app, MCP app, MCP server or use the Skybridge framework.
Guide developers through creating MCP apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/widgets, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a MCP app, MCP server or use the Skybridge framework.
Build and run evaluators for AI/LLM applications using Phoenix.
Enables Claude to create, manage, and organize events in Google Calendar via Playwright MCP
Guide for creating AI subagents with isolated context for complex multi-step workflows. Use when users want to create a subagent, specialized agent, verifier, debugger, or orchestrator that requires isolated context and deep specialization. Works with any agent that supports subagent delegation. Triggers on "create subagent", "new agent", "specialized assistant", "create verifier".
Parallel execution engine for high-throughput task completion
Full autonomous execution from idea to working code